Search engines typically provide a source of indexed documents from the Internet (or an intranet) that can be rapidly scanned in response to a search query submitted by a user. As the number of documents accessible via the Internet grows, the number of documents that match a particular query may also increase. However, not every document matching the query is likely to be equally important from a user's perspective. A user may be overwhelmed by an enormous number of documents returned by a search engine, unless the documents are ordered based on their relevance to the user's query. One way to order documents is the PageRank algorithm more fully described in the article “The Anatomy of a Large-Scale Hypertextual Search Engine” by S. Brin and L. Page, 7th International World Wide Web Conference, Brisbane, Australia and U.S. Pat. No. 6,285,999, both of which are hereby incorporated by reference as background information.
In addition to responding to search queries, search engines can also proactively identify and recommend popular queries. The recommendation techniques developed to date, however, have only limited utility because they often recommend queries that are not of interest to the user.
Thus, it would be highly desirable to find new, more efficient and accurate ways to provide recommendations of popular search queries.